Report Assessment Ch2 Technology Visions

Data Management Report

Author

Affiliation

Rainer M. Krug

Senckenberg

Abstract

A short description what this is about. This is not a tracditional abstract, but rather something else …

Working Title

IPBES_TCA_Ch2_technology

Code repo

Github - private

Build No: 19

%The BuidNo is automatically increased by one each time the report is rendered. It is used to indicate different renderings when the version stays the same%.

Introduction

The following steps will be done in documented in this report:

• Do asearch with vision_st, technology_st, and vision_st AND technology_st and determine the number of hits
• Identify sub-fields (in topics) of hits
• Check filtering using sub-fields
• Should the complete vision_st AND technology_st be downloaded, or only a random subsets? Suggestiopn: 10 sets of 100 random works?

Step 1: Determination of numbers

The search terms are based on the shared google doc. They are cleaned up for the usage in OpenAlex.

Vision

The search terms is vision

#|

vision_count <- openalexR::oa_fetch(
    title_and_abstract.search = compact(vision_st),
    count_only = TRUE,
    verbose = TRUE
)[, "count"]

Technology

The search terms is technology

#|

technology_count <- openalexR::oa_fetch(
    title_and_abstract.search = compact(technology_st),
    count_only = TRUE,
    verbose = TRUE
)[, "count"]

Vision AND technology

The search term is vision AND technology

Count

#|

vision_technology_count <-
    openalexR::oa_fetch(
        title_and_abstract.search = compact(paste0("(", vision_st, ") AND (", technology_st, ")")),
        count_only = TRUE,
        verbose = TRUE
    )[, "count"]

Count Subfields

#|

vision_technology_subfields <- openalexR::oa_query(
    title_and_abstract.search = compact(paste0("(", vision_st, ") AND (", technology_st, ")")),
    group_by = "primary_topic.subfield.id",
    verbose = TRUE
) |>
    openalexR::oa_request() |>
    dplyr::bind_rows() |>
    dplyr::arrange(key)

## clean up missing or wrong vision_technology_subfields$key_display_name
need_cleaning <- is.na(vision_technology_subfields$key_display_name) |
    !is.na(as.numeric(vision_technology_subfields$key_display_name))

Warning: NAs introduced by coercion

fine <- !need_cleaning

vision_technology_subfields <- vision_technology_subfields |>
    dplyr::filter(fine) |>
    dplyr::select(key, key_display_name) |>
    dplyr::distinct() |>
    merge(y = vision_technology_subfields[need_cleaning, -2], by = "key") |>
    dplyr::bind_rows(vision_technology_subfields[fine, ]) |>
    dplyr::group_by(key, key_display_name) |>
    dplyr::summarize(count = sum(count))

Results

Number of hits

• vision:: 105,125,052 hits
• technology: 14,665,760 hits
• vision AND technology: 11,793,910 hits

Subfields

The subfields are based on the main topic assigned to each work. There are other topics also assigned, but this one has been identified as the main topic by an algorythm. count is the number of works in the vision AND technology corpus which have been assigned to the subfield.

Please take a look at these subfields of the topics to identify the ones to be filtered out.

The easies would be to download the Excel file through the button and to mark the subfields to be filtered out.

IPBES.R::table_dt(vision_technology_subfields, fixedColumns = NULL, fn = "Vision Technology Subfields")